The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().

weight : string or None, optional (default=’weight’)

The edge data key used to provide each value in the matrix.
If None, then each edge has weight 1.

Returns :

A : SciPy sparse matrix

Adjacency matrix representation of G.

See also

to_numpy_matrix, to_scipy_sparse_matrix, to_dict_of_dicts

Notes

If you want a pure Python adjacency matrix representation try
networkx.convert.to_dict_of_dicts which will return a
dictionary-of-dictionaries format that can be addressed as a
sparse matrix.

For MultiGraph/MultiDiGraph with parallel edges the weights are summed.
See to_numpy_matrix for other options.

The convention used for self-loop edges in graphs is to assign the
diagonal matrix entry value to the edge weight attribute
(or the number 1 if the edge has no weight attribute). If the
alternate convention of doubling the edge weight is desired the
resulting Scipy sparse matrix can be modified as follows: